In medical imaging a (frequent) problem which occurs is that of having to rapidly yet still precisely register intra-operative image data sets with pre-operative 3D image data sets; there being various reasons for this:
1. Intra-operative image data sets which are obtained as so-called real-time images during an intervention, usually with the aid of fluoroscopic imaging, are primarily used for navigation of surgical instruments (e.g. in the head or the heart). These types of fluoroscopic 2D images (recorded with a C-arm for example) are rapidly available, the imaging technology minimizes the radiation dosage for patient and doctor. Interventional equipment (operation instruments, catheters, guide wires etc.) are shown as high-resolution images almost in real time. However such fluoroscopy images—compared to images of 3D imaging modalities (e.g. CT, MRT, 3D angio)—do not show any spatial details. In the prior art the spatial information has been recovered by registering 3D images (for example from a CT, 3D angio or MR tomography) recorded pre-operatively with two-dimensional intra-operative radiological images and underlaid with these images, a process which is also referred to as co-registration. With this type of registration the direction from which a 3D volume must be projected so that it can be brought into alignment with the intra-operative 2D image must be determined.
2. Intra-operative image data sets can also be functional nuclear-medical 3D image data (e.g. PET or SPECT images) which supply a description of metabolic functions or processes of the body. One of the uses of such image data is to visualize anatomical regions which exhibit an abnormal metabolism, such as tumors for example, and to determine their size and activity. However these functional 3D images provide little information about the patient's anatomy in which the pathogenic region (e.g. the tumor) is embedded, which is a problem when such functional image data is used exclusively in respect of diagnostics, therapy planning and therapy, especially since the spatial resolution of this image data is greatly limited. It is therefore difficult to localize a tumor exactly solely using nuclear-medical 3D image data (SPECT, PET), since the exact spatial relationship to the patient anatomy is missing from this data. However this spatial relationship can also be re-established by registration (and fusion) of the functional 3D image data with high-resolution morphological 3D image data.
The problem from the medical technology standpoint—with 2D radiological images just as with 3D images—lies in the registration with the high-resolution morphological 3D images. There are various approaches to this problem in the prior art, which are based for example on user interaction (setting of landmarks in both images) or for example on minimizing the differences in intensity.
In particular the problem of the registration of functional 3D image data with morphological 3D image data is currently resolved in different ways
A) The so-called “Multi-Modality 3D-3D registration” combines complementary information which was generated by different imaging modalities. Thus for example anatomical information from CT, MR, C-arm rotation angiography or 3D ultrasound image data can be combined with functional information of fMRI, PET, SPECT image data or of functional mapping modalities such as EEG or MEG.
B) Functional and morphological image data can (also) be fused with the aid of landmark-based (feature-based) or image-based registration algorithms, with a software implementation of specific algorithms being used as the basis in this case.
Such a software-based registration only operates reliably if enough common image information is available as regards functional and morphological image data, which is not always the case. Although the performance of this software registration is satisfactory as a rule for diagnostic applications, an improvement would be desirable however. It should also be mentioned that the accuracy of the individual registration results in the individual case cannot be quantitatively recorded but mostly only assessed visually since the actual desired result is not known in advance.
C) To ameliorate the problem of approach B) and to achieve improvements with regard to the imaging clinical workflow, combinations of anatomical imaging modalities and functional imaging modalities have since been made commercially available: e.g. the “CT-PET Biograph system” or the “CT-SPECT Symbia system” from Siemens.
In general the problem of registration or the overlaid representation (fusion) of anatomical and functional 3D image data is considerably simplified by such a modality combination. However the costs of providing a modality combination are extremely high and in addition it is not currently possible to obtain combinations which are more subject to failure but are still sensible and desired (e.g. PET+MRT, SPECT+MRT, ultrasound doppler+MRT, MEG+MRT etc.).
The disadvantage of the existing method is on the one hand a certain imprecision which is not to be tolerated and on the other hand the high time overhead sometimes involved in these methods. It is namely desirable for these methods to be able to react as exactly and quickly as possible to any patient movements. Exact and fast registration, especially of functional with morphological 3D image data, turns out to be extremely difficult in practice however since only a small number of common factors exist between these two complementary image data categories.
A method is disclosed in “Clinical Experience with a High Precision Image-guided Neurosurgery system” by E. Grimson, M. Leventon, G. Ettinger, A. Chabrerie, F. Ozlen, S. Nakajima, H. Atsumi, R. Kikinis, P. Black, Lecture Notes in Computer Science (Springer), Proc. MICCAI'98; October 1998, pages 63ff in which, using laser scanners (laser on stepping motor in combination with a video camera) a surface area of the object to be examined is determined which is used as a basis of a registration between a pre-operative image containing this surface area and an interoperative 2D or 3D image data set at least partly containing this area. However the scan duration of a laser scan is specified as around 30 seconds which makes the method susceptible to movement artifacts and it is unsuitable in particular for series of functional recordings or rapid instrument tracking.